Technological Forecasting & Social Change 125 (2017) 58–65

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Technological Forecasting & Social Change

journal homepage: www.elsevier.com/locate/techfore

The sharing economy in social media: Analyzing tensions between market MARK and non-market logics

⁎ Christofer Laurella, , Christian Sandströmb a Stockholm School of Economics Institute for Research and Jönköping International Business School, Box 6501, SE-113 83 Stockholm, Sweden b Chalmers University of Technology and the Ratio Institute, Vera Sandbergs Allé 8B, SE-412 96 Göteborg, Sweden

ARTICLE INFO ABSTRACT

Keywords: How is the sharing economy framed and who are the main actors driving current developments? Utilizing Social Sharing economy Media Analytics (SMA) for institutional analysis, we track the formation of the sharing economy in Sweden, its Market logic actors and their impact. Our findings reveal that the sharing economy in Sweden currently encompasses a wide Non-market logic variety of both non-market and market practices. Discussions concerning commercial exchanges, the role of Collaborative consumption profit-driven firms such as Uber and Airbnb, and the emergence of a market logic has created a state of in- Platform capitalism stability. Our results point at several unresolved issues, such as taxation and regulation. Based on these findings, Social media analytics fi Institutional analysis we suggest an expanded de nition of the sharing economy which incorporates both market and non-market Uber logics. Airbnb

1. Introduction Sweden whilst also pointing out the main actors driving current de- velopments. Utilizing Social Media Analytics (SMA) for institutional Sharing-economy platforms are gaining momentum in several in- analysis, we conceptualize the sharing economy as an organizational dustries, offering the potential of efficient utilization of resources, novel field and describe its current state in Sweden, its actors and their im- value creation, and technological disruption whilst also generating in- pact. Our findings reveal that the sharing economy in Sweden currently stitutional turbulence. spans a wide variety of both non-market and market practices and that Being in its infancy, the field is still riddled with controversies and the field is currently characterized by instability and tension. Moreover, ambiguities. On one hand, previous research has documented how the discussions that concern the framing of the sharing economy are cur- notion of a sharing economy and the related term collaborative con- rently dominated by profit-driven firms, most notably Uber and Airbnb. sumption emerged as descriptions of online activities such as content Our data points at several ambiguities that remain unsolved, e.g., sharing, collaborative encyclopedias like Wikipedia, file sharing and taxation and regulation. In view of these findings, we contribute to open-source software, where people are driven by a combination of extant literature by providing a structured analysis of the state of the financial and non-financial motives (Hamari et al., 2015). On the other sharing economy that illustrates its diverse character. Our findings hand, the term sharing economy has become increasingly associated show the importance of incorporating both market and non-market with a form of platform capitalism where profit-driven entrant firms logics into the conceptualization of this phenomenon, and therefore we create two-sided markets (Dreyer et al., 2017) and monetize the in- suggest an expanded definition of the sharing economy toward the end teraction between buyers and sellers (Murillo et al., 2017). This form of of the paper. As popular accounts on the sharing economy in other sharing economy is a truly disruptive force, not only for established countries indicate similar patterns regarding unsolved ambiguities, the firms but also for current institutions, as issues such as tax evasion and results in this paper can, to a certain extent, be utilized to approach regulatory compliance remain unsolved (Laurell and Sandström, 2016, other national contexts. forthcoming). Still being in a fluid state, it is presently unclear how the The paper begins with a brief background concerning the con- sharing economy is framed. Additionally, more empirical data is needed temporary state of the sharing economy and the conceptual approach regarding ongoing developments within this rapidly transforming area employed throughout the article. Subsequently, the method is de- of society. scribed. Next, our results are presented and analyzed. Finally, we pro- In this paper, we explore how the sharing economy is framed in vide a concluding remark together with directions for future research.

⁎ Corresponding author. E-mail addresses: [email protected] (C. Laurell), [email protected] (C. Sandström). http://dx.doi.org/10.1016/j.techfore.2017.05.038 Received 24 June 2016; Received in revised form 24 May 2017; Accepted 31 May 2017 Available online 16 June 2017 0040-1625/ © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). C. Laurell, C. Sandström Technological Forecasting & Social Change 125 (2017) 58–65

2. Elements of the topic and conceptual approach subjected to change. Those actors which are constrained by the same institutional set-up are together referred to as an organizational field The notion of the sharing economy has become subject to a lot of (DiMaggio and Powell, 1983). The process of organizational field for- attention and even hype in recent years (Felländer et al., 2015). The mation is dynamic, as fields over time aggregate specific compositions term sharing economy and the related notion of collaborative con- of actors, a certain degree of opposition among them, their relative sumption both have their origins in Information and Communication position within a given field, and their functional weight or extent of Technology (ICT)-enabled interactions between users on the internet power within that field (Bourdieu, 1984, 1990). The degree of stability (Botsman and Rogers, 2010; Kaplan and Haenlein, 2010; Wang and of a given field depends upon the extent of power and potential influ- Zhang, 2012) which offer the potential of transitioning societies into a ence an actor has upon other actors to change the rules of the game or post-ownership economy (Belk, 2014). Whilst the sharing economy and impose a new set of conditions that change behavior within a given collaborative consumption still tend to be vaguely defined, one of the field (Bourdieu, 1984, 1990). Thus, actors' actual behaviors depend recently suggested definitions of how the two notions can be related to upon every part of the field, because the respective parts of a specific each other was presented by Möhlmann: field are mutually interdependent (Lewin, 1939). Organizational fields are, therefore, subject to what scholars refer to as the paradox of em- “Collaborative consumption, often associated with the sharing economy, bedded agency (Seo and Creed, 2002). takes place in organized systems or networks, in which participants When analyzing a specific organizational field at a certain point in conduct sharing activities in the form of renting, lending, trading, bar- time, three dimensions are usually studied: (1) the state of the field in tering, and swapping of goods, services, transportation solutions, space, which actors operate; (2) how meanings among actors are diffused; and or money.” (2015, p. 193) (3) relations between actors (Hardy and Maguire, 2008). The term sharing economy has also been used to advocate the shift First, the coexistence of multiple institutional logics in an organi- toward a more sustainable economy and the emergence of a colla- zational field, as seems to be the case for the sharing economy, usually borative commons (Parguel et al., 2017; Bauwens and Kostakis, 2014). represents an enabling condition for changes within organizational This conceptualization of a sharing economy adapts a non-market logic fields (Clemens and Cook, 1999; Sewell, 1992). Additionally, institu- where exchanges are not primarily coordinated via the price me- tional change is more easily accomplished in fields that are emerging as chanism and where actors are largely motivated by factors other than institutionalized practices have not yet been established or stabilized. profit, e.g., altruistic values related to sharing, helping others, and This is particularly the case for emerging fields characterized by fluid contributing to a more sustainable way of life (Prothero et al., 2011; relationships, conflicting values, and absence of norms. Under these Sacks, 2011). circumstances, actors can engage in actions which can transform the The social movement described above stands in contrast to current structure of organizational fields (Fligstein, 1997). developments of the sharing economy. In recent years, a form of plat- Second, institutional change has been shown to manifest as ongoing form capitalism has emerged, using the notion of a sharing economy. and complex struggles over meaning among actors (Czarniawska and Firms such as Airbnb, Uber, and TaskRabbit enable individuals to ex- Joerges, 1996; see also Berger and Luckmann, 1966; Phillips and change services via a platform. These firms have received hundreds of Malhotra, 2008). From this perspective, institutions are created and millions in venture capital (Alsever, 2013), are driven by profit(Slee, formed as meanings are shared and taken for granted, but also “emerge 2016), and compete with established firms, often by creating turbu- from novel interpretations and ensuring struggles over meaning, although it lence and possibly redefining notions of work and employment in the also recognizes that, because meanings of existing practices are supported by long run. existing logics, myths and discourses, they may not be easily displaced” Taken together, the notion of a sharing economy seems to be riddled (Hardy and Maguire, 2008, p. 205). In this context, Munir (2005) ar- with tensions between non-market logics of idealism and a form of gued that the way in which events and changes are interpreted and platform capitalism driven by for-profit firms (Murillo et al., 2017; given meaning is one of the central aspects of processes of institutional Schor, 2014). With this tension in mind, more knowledge is needed entrepreneurship. about how the sharing economy is framed and which actors are driving Third, actors who benefit from a certain structural arrangement will current developments. Therefore, the research question we set out to be more likely to act to change organizational fields (Maguire et al., answer is formulated as follows: How do online discussions in social media 2004). Moreover, being in the periphery of an established field usually reflect market and non-market logics in the field of the sharing economy? implies less embeddedness in an institutional arrangement (Leblebici To explore how the sharing economy is framed whilst also pointing et al., 1991). Other scholars have regarded institutional change as lar- out the main actors related to this framing, we conceptualize the phe- gely a social challenge (Fligstein, 1997) where actors' abilities to mo- nomenon of the sharing economy as an emerging organizational field tivate and orchestrate is crucial in accomplishing change. In this con- that, based on extant literature, encompasses two institutional logics text, the discursive dimension has been highlighted as particularly that continuously contribute to redefining the boundaries of the field. important (Creed et al., 2002; de Holan and Phillips, 2002; Dorado, The underlying rationale for doing so is that many of the developments 2005). of the sharing economy identified by previous literature resemble the When taken together, these three dimensions—the state of the field, evolution of organizational fields (Mair and Reischauer, 2017). Based how meanings among actors are diffused, and relations between ac- on this conceptualization, the sharing economy is approached as an tors—can be utilized to guide the structure of institutional analysis and, organizational field that seems to be depicted in extant literature as a by doing so, study the framing of an emerging organizational field and set of organizations displaying diverse approaches (DiMaggio and how institutional logics redefine the boundaries of that field. Powell, 1983), operating within a seemingly nested system (Holm, 1995) and, based on issues related to the non-market or market logics 3. Method (Thornton and Ocasio, 1999), potentially functioning as a basis out of which the field forms (Hoffman, 1999, cf. Bourdieu, 1984, 1990). To explore how the sharing economy is framed and identify which More specifically, extant literature illustrates that the introduction actors are driving current developments, SMA was employed for in- of novelty, such as a new technology, frequently results in the emer- stitutional analysis. Online data collection has been utilized increas- gence of new business models as well as institutional upheaval ingly in recent years, and this development has resulted in the emer- (Bohnsack et al., 2016; Ernkvist, 2015; Laurell and Sandström, 2014). gence of SMA, an interdisciplinary approach that seeks to combine, When this occurs, institutions, i.e., formal and informal “humanly de- extend, and adapt methods for analysis of social media data (Stieglitz vised constraints that structure human interaction” (North, 1990, p. 3), are et al., 2014). As social media represents “a kind of living lab, which

59 C. Laurell, C. Sandström Technological Forecasting & Social Change 125 (2017) 58–65 enables academics to collect large amounts of data generated in a real-world Table 1 environment” (Stieglitz et al., 2014, p. 90), it is ideal for studying a Collected and publicly posted user-generated posts per social media platform. contemporary phenomenon such as the sharing economy in which Social media Frequency Share considerable institutional change takes place (Laurell and Sandström, 2016), as this form of data contains expressions that reveal how a Blog 32 3.2% specific field is framed and which actors contribute to this framing. Facebook 75 7.5% Moreover, the unobtrusive character of SMA makes it highly suitable Forum 1 0.1% Instagram 31 3.1% for analyzing how transformations take place, as discussions con- Twitter 855 85.6% tinuously add to the framing of a studied object. YouTube 5 0.5% Total 999 100% 3.1. Data collection

In terms of data collection, the fragmented social media landscape content relating to phenomena other than the one in question. This fi and lack of standardized ways of gaining access to social media plat- review identi ed 35 user-generated posts in the data set that concerned forms is one of the main challenges facing SMA researchers. Due to other issues. These posts were, therefore, excluded from the data set, increased interest among researchers, however, a plethora of services resulting in a total of 999 remaining user-generated posts. Table 1 offering structured access to user-generated content across platforms presents the distribution of these posts across social media platforms. As has emerged in recent years. the table illustrates, a considerable share of the material was generated We used a service called Notified to collect data. The service cap- from Twitter over the studied period, whilst other major social media tures user-generated content published on a diverse set of social media outlets generated a relatively modest material in comparison. In platforms. When using the tool, the researcher first enters a set of alignment with the aim of this paper, to explore how the sharing keywords along with the language or set of languages for data collec- economy is framed in Sweden, combined with the underlying principle tion. This becomes an important issue to consider as particular key- of SMA relating to the study of natural occurrences in real-world en- words can have either a narrow or broad set of associated connotations vironments (Stieglitz et al., 2014), no action was taken with regard to in different languages. Whilst a broad set of languages provides a po- the distribution of data across social media platforms. This decision was tentially richer data set, filtering the data collection process to a specific taken because of our ambition to capture ways in which the sharing language and user origin allows for a more focused approach that can economy was framed throughout the social media landscape, regardless fi be of particular relevance for studies of emerging phenomena. When of which speci c social media platforms were utilized. Using the same fi keywords and language settings have been entered, all publicly posted rationale, no speci c action was taken concerning reposts (such as re- “ ” user-generated content from Twitter, Instagram, Facebook, blogs, tweets on Twitter or regrams on Instagram), as these instances also forums, and YouTube is collected in a real-time database. The tool contribute to the framing of the phenomenon at hand. therefore allows researchers to collect data in a structured manner from In the next phase, a sequential analysis model in four steps guided fi a relatively broad set of social media applications found in the social the institutional analysis of the data set. More speci cally, this se- fi media landscape. This also means the researcher does not need to rely quential analysis framework was developed in line with the identi ed fi on often inaccurate digital data collection methods, such as scraping characteristics related to institutional changes of organizational elds techniques (Stieglitz et al., 2014). The use of external services to collect presented by Hardy and Maguire (2008), including the following four data when applying SMA does, however, come with its own set of dimensions: challenges. This is especially the case regarding how data is imported fi from each respective social media platform to a certain service, as no (1) the state of the organizational eld common standard for the export of data across social media platforms (2) institutionalized practices ff has yet emerged (Stieglitz et al., 2014). (3) meanings di used among actors For this study, the Swedish words for “sharing economy” and “the (4) actors and their position. sharing economy” were used, both of which are direct translations from fi English. The keywords were entered into the service on March 14, First, the state of the sharing economy as an organizational eld was 2016. Thereafter, data was collected up until May 14, 2016, generating explored by reviewing thematic categories concerning how the sharing a data set of 1034 social media posts over a two-month period. The data economy was discussed and framed in the data set. All user-generated set only comprised user-generated content written in Swedish. The ra- content included in the data set was analyzed by focusing on written ff tionale for this was twofold: first, the Swedish word for the sharing text only in an e ort to make the collected content comparable across economy has relatively few associated or alternative connotations. platforms. This meant that text from, for example, Twitter and Therefore, user-generated content including these two keywords was Instagram were treated equally across platforms, as the study of inter- ff assumed to have a relatively high degree of relevance to the phenom- platform variances of how the sharing economy is framed on di erent fi enon in question; second, Sweden is one of the countries that frequently platforms was outside the scope of this study. Based on a rst review of fi tops the global rankings of digital technology usage as well as high- the data set using this text-based approach, ve main thematic cate- fi speed internet access, making its social media landscape vibrant gories were identi ed: (1) the sharing economy as a phenomenon; (2) (Findahl and Davidsson, 2015) and, therefore, particularly suitable for user experiences associated with the sharing economy; (3) the sharing fi SMA. economy in relation to speci c actors; (4) sectors of the sharing economy; and (5) societal consequences of the sharing economy. After fi 3.2. Data analysis these dimensions had been identi ed, the material was coded accord- ingly throughout the data set. After data collection had been completed, the data set was analyzed Second, institutionalized practices were analyzed to address how fi fl by applying content analysis (Silverman, 2006). This was carried out by the development of the eld might give rise to con icting values using the collected user-generated content as the object of analysis. (Maguire et al., 2004). We operationalized practices utilizing compo- fi More specifically, only structured (i.e., account details) and un- nents of Möhlmann's de nition which suggests that common practices “ structured (i.e., textual content) data associated with the collected user- found in the sharing economy encompass renting, lending, trading, ” generated content were analyzed. bartering, and swapping (2015, p. 193). The initial review generated fi In the first step, the data set was reviewed to exclude user-generated three practices that the de nition encompasses in terms of renting,

60 C. Laurell, C. Sandström Technological Forecasting & Social Change 125 (2017) 58–65 lending, and swapping and three additional practices in terms of gifting, 4.1. Framing and defining the sharing economy selling, and sharing. Based on these six identified practices, the material was reviewed again by coding user-generated content that articulated Table 2 presents the five main thematic categories around which the either one specific practice or a set of specific practices. sharing economy is discussed and framed. Contents regarding the Third, meanings diffused among actors taking part in framing the sharing economy as a phenomenon and the societal consequences to- sharing economy, that could function as a source of institutional change gether represent a considerable share of the total material (81.9%). This (cf. Czarniawska and Joerges, 1996; Munir, 2005; Phillips and indicates that the sharing economy is, arguably, considered a novelty Malhotra, 2008), were analyzed by identifying specific issues being and that the societal consequences of this emerging organizational field discussed in the material. In total, 21 issues were generated in a first are still unclear. In addition to the examples presented in the table, two review which covered such issues as taxation, job creation, and con- more examples regarding the sharing economy as a phenomenon and its sumer protection. Based on these issues, user-generated content that societal consequences were published in a blog on May 11, 2016 and on contained references to the identified issues was coded thereafter. Twitter on April 4, 2016: Fourth, actors and their position play a central role in institutional “We live in exciting times. Technology is advancing at a furious pace and change (cf. Hardy and Maguire, 2008; Munir, 2005). Actors that are at the same rate change our behavior and our opportunities. It is no considered part of the sharing economy by social media users were longer strange to take a taxi with someone who is not really a taxi driver identified by initially reviewing the material for references to specific or to live with a stranger in a foreign country.” sharing-economy actors. This review identified 31 individual sharing- economy actors in total. Thereafter, all user-generated content was “The sharing economy needs rules that make it easy to do right, but it reviewed to identify content containing specific references to the must fundamentally be seen as an opportunity to create a better society.” identified actors. In so doing, and by coding instances where references Of the 999 posts analyzed, 314 concern various sharing-economy to these actors were present, it was possible to assess the relative im- practices. As Table 3 illustrates, there is a relatively wide variety of portance of each identified actor in the wider framing of the sharing practices that are interrelated to the sharing economy from the per- economy carried out by social media users. spective of users of social media. Two examples of how practices such Having identified which actors were referred to as part of the as swapping and renting are articulated in the data were published on sharing economy, this analysis was followed by an interrelated review Instagram on March 22, 2016 and in a blog on May 2, 2016: of which actors took part in framing the sharing economy (cf. Czarniawska and Joerges, 1996; Hardy and Maguire, 2008). In total, “New collection in the shop every week! Here, 300–400 garments are 676 unique actors who had posted user-generated content were iden- swapped per week.” tified in the data set. When carrying out the analysis, a cutoff was used, “As the sharing economy becomes increasingly popular, almost every set to a minimum of three user-generated posts published over the fourth car owners today are willing to rent out their own car. But for studied period per unique actor. This generated 73 unique actors that, anyone who rents and are renting out, there are several things that are in total, had contributed 337 user-generated posts. Thereafter, these 73 important to consider.” unique actors were categorized into actor groups, with the purpose of analyzing which group of actors contributed most to the framing of the Almost two-thirds of these posts (64.6%) concern selling and 24.8% sharing economy over the study period. Some individual actors seemed deal with renting. In total, almost 90% of the content about sharing- to play multiple roles, thereby creating ambiguity about accurate ca- economy practices are, therefore, related to commercial exchanges. tegorization. In these cases, the main role the actor played, as stated by Sharing, swapping, lending, and gifting only make up around 10%, an organization's mission statement for example, guided the final ca- which indicates that discussions are dominated by market exchanges. tegorization. Therefore, our results suggest that informal institutions (North, 1990) related to the sharing economy—in the real sense of the term—are currently in a state of instability as tensions exist between market and 4. Results and analysis non-market logics. Furthermore, the presented results illustrate that the sharing and sharing-oriented practices identified by Möhlmann (2015) Our results are presented in two steps. First, results on the state of do not dominate the field. Instead, the total set of practices included in the organizational field of the sharing economy are presented, along the sharing economy has expanded, and one of the novel practices in- with institutionalized practices of the sharing economy and various cluded in this expanded set, selling, is also the most dominant practice issues being discussed. This is followed by results concerning which (Table 3). We also observe that gifting and sharing are included in our actors are perceived as part of the organizational field of the sharing sample, albeit only at very low frequencies. economy by social media users, and which actor groups contribute the Whilst the content regarding practices suggests a shifting consensus most to its framing. about actual behavior, the fact that 96 posts in Table 4 are concerned with the definition of the sharing economy indicates that vibrant

Table 2 Identified thematic categories.

Dimension Frequency Share Data example

Phenomenon 318 31.8% “Having a lot of stuff that is rarely used will no longer be a status symbol. Instead, we rent, borrow, give away, replace, or buy second- hand. It is a phenomenon that goes by many names.” (Facebook, April 9, 2016) User experience 16 1.6% “A week ago, I took my first ride with Uber Pop. I have previously been a bit of a semi-opponent. […] But I had a good friend visiting me and she made me download the app and order my first ride with Uber. It worked just fine. The driver was nice. It went smoothly. The car did not smell like cigarettes. I was simply more than satisfied.” (Blog, May 11, 2016) Organizational 63 6.3% “Bonsai is growing with the sharing economy in Sweden!” (Twitter, May 11, 2016) Sector 102 10.2% “Now that the sharing economy is emerging, condominiums and rental apartments will disappear. Instead, you will do a search using an app when you need a roof over your head.” (Twitter, April 8, 2016) Societal 500 50.1% “The sharing economy might very well be good, but do we want venture capital firms to control the economy without paying taxes?” (Twitter, May 2, 2016) Total 999 100.0%

61 C. Laurell, C. Sandström Technological Forecasting & Social Change 125 (2017) 58–65

Table 3 “ Identified practices. The problem basically is that the legislation has not caught up. It needs to be adapted to the sharing economy.” Practices Frequency Share “At one time the Social Democrats wanted to ban satellites. Now they do Gifting 1 0.3% their best to ban off the new sharing economy, with services like Uber and Renting 78 24.8% Airbnb. But technology always wins over politics.” Lending 8 2.5% Selling 203 64.6% “The sharing economy must be embraced, and the biggest threat is passive Sharing 14 4.5% politicians.” Swapping 10 3.2% Total 314 100.0% Therefore, we can conclude that the emergence of a market logic has instilled controversies. As a field, the sharing economy is presently characterized by tensions between market and non-market logics. Table 4 Concerning formal institutions, it is difficult at this point to discern any Identified issues. emerging consensus. The results above, therefore, illustrate the di- Issue Frequency Share versity and associated tensions of the sharing economy. The discussions reviewed above also show that there are conflicting values and norms Taxation 158 28.8% which, in turn, indicates ongoing institutional change (Clemens and Definition 96 17.5% Cook, 1999; Fligstein, 1997). Political 64 11.7% Regulatory evolution 49 8.9% Environment 31 5.6% 4.2. Actors and positions in the sharing economy Economical evolution 25 4.6% Competition 21 3.8% Regulation 21 3.8% Table 5 presents those actors identified by social media users as part Entrepreneurship 13 2.4% of the sharing economy and those actors' associated frequency and Job creation 10 1.8% share of the material. Two examples of how this integration of sharing- How to 9 1.6% economy actors by social media users is manifested in the data were Working conditions 9 1.6% Consumption behavior 7 1.3% published on Twitter on March 22, 2016 and April 24, 2016: Legislation 6 1.1% “ ” Sustainability 6 1.1% More Uber to the people! #sharingeconomy. Technological evolution 5 0.9% “Really nice! - > Their startup Tipp Tapp will help you to get rid of Ownership 4 0.7% ” Policy instruments 4 0.7% garbage #sharing economy. Resource utilization 4 0.7% As the table illustrates, from the perspective of users, the dom- Black jobs 3 0.5% fi fi Consumer protection 2 0.4% inating sharing-economy actors are private, pro t-maximizing rms Equality 2 0.4% (Uber and Airbnb), together making up more than 60% of the content. Total 549 100.0% Table 5 Identified actors considered to be part of the sharing economy by social media users. discussions about the meaning of the term are ongoing. Several posts in this category express discontent over how the term is used to describe Actor Frequency Share activities that are not really related to sharing, and several other posts Uber 189 47.4% deal with what should be included in the notion, and who should be Airbnb 69 17.3% considered part of the sharing economy. The following examples from Tinder 22 5.5% Twitter, published on May 11 and 12, 2016, are illustrative: Workaround 16 4.0% Hoodifood 12 3.0% “UberPop is not part of the sharing economy, but part of the tax fiddler Delbar 11 2.8% economy. Society does not need organized illegal taxicabs.” Airdine 10 2.5% DriveBack 8 2.0% “New services such as UberPop need to meet with new laws that allow the Airpnp 6 1.5% sharing economy.” Bonsai 6 1.5% Budbee 4 1.0% There are also examples where users argue that the notion of the Rentl 4 1.0% sharing economy has become hard to apply due to ambiguity. Two such Tipptapp 4 1.0% examples from Twitter were published respectively on May 9 and May Gigamunch 4 1.0% Car2go 2 0.5% 11, 2016: Bubbler 2 0.5% Gomore 2 0.5% “Sharing economy/carpooling are loaded words, should not be used KeyPit 2 0.5% ” perfunctory. Meetred 2 0.5% “ … ” MöbLerum 2 0.5% Words matter. [ ] Scrap the word sharing economy. Onefinestay 2 0.5% Turning from informal to formal institutions (North, 1990), we can Retoy 2 0.5% Taskrunner 2 0.5% see in Table 4 that a large percentage of the analyzed content concerns TaskRabbit 2 0.5% formal institutions associated with the sharing economy. The fact that Tori.fi 2 0.5% more than 50% of all content published about the sharing economy is Huuto.net 2 0.5% concerned with issues such as regulation and taxation indicates that Kimppakyyti 2 0.5% Urb-it 2 0.5% plenty of social media attention is focused not only on informal in- Instawork 2 0.5% stitutions but also on formal institutions. The following three posts, Selfiejobs 2 0.5% published on Twitter and Facebook on March 15, 2016 and May 12, Grannsaker 2 0.5% 2016, exemplify ongoing discussions about formal institutions: Total 399 100.0%

62 C. Laurell, C. Sandström Technological Forecasting & Social Change 125 (2017) 58–65

Table 6 4.3. The framing of the sharing economy Actor groups contributing to the framing of the sharing economy, their frequency, and share of published user-generated content compared to the total material. Taken together, the presented results illustrate the varying ways in

Actor Frequency Share which the sharing economy in Sweden is framed and which actor groups contribute to this framing. More specifically, considerable at- Sharing-economy actors 58 17.2% tention is devoted to the phenomenon as such but also the implied Politician 31 9.2% societal consequences (cf. Tables 2 & 4). Among the issues character- Research institute 23 6.8% Non-professional 20 5.9% izing the discussions, a relatively wide range of societal issues can be Professional expert 17 5.0% identified (Table 4). Municipality official 13 3.9% One underlying reason for this framing seems to be that Uber and User 13 3.9% Airbnb on the one hand are perceived and framed by social media users Governmental agency representative 12 3.6% as part of the sharing economy. On the other hand, these actors are Researcher 11 3.3% Entrant representative 10 3.0% subject to resistance by social media users who argue that they should Newspaper 9 2.7% not be understood as part of the phenomenon in question. Taken to- Political party 8 2.4% gether, this pattern illustrates the varying meanings and values that Science park 8 2.4% encompass the term sharing economy from the perspective of social Incumbent employee 7 2.1% Interest group representative 7 2.1% media users and, consequently, the ways in which the sharing economy Media monitoring service 7 2.1% is framed. For instance, the empirical examples provided in Table 4 Governmental agency 6 1.8% illustrate how these actors have met resistance as they are perceived to Innovation advisor 6 1.8% represent a market-oriented logic and because these users seem to be News service 6 1.8% proponents of non-market connotations and origins of the phenomenon. Municipality initiative 5 1.5% Think tank 5 1.5% Even though this may be the case, another illustration is found in Research institute representative 5 1.5% Table 5 that shows how these perceived market-oriented actors still Enthusiast 4 1.2% occupy a dominant position vis-à-vis actors who engage in non-market- Professional institute 4 1.2% oriented practices, based on the overall perception of social media users Business park 3 0.9% Conference 3 0.9% (see also Table 3). Employers' organization 3 0.9% Regarding actor groups that contribute to the framing of the sharing Employers' organization representative 3 0.9% economy, however, Table 6 shows that the field is currently scattered in Environmental advisor 3 0.9% the sense that many actors take part in the collective framing. This Federation of business owners 3 0.9% observation suggests that an important reason why the exhibited am- Incumbent representative (Employers' organization) 3 0.9% Journalist 3 0.9% biguity exists is related to the plethora of actor groups who, together, Municipality representative 3 0.9% are perceived as part of the sharing economy (Table 5) as well as among Sustainability advisory 3 0.9% actor groups who contribute to the framing of the sharing economy Sustainability advisory representative 3 0.9% (Table 6). An additional explanation is probably that actors who are Sustainability magazine 3 0.9% Union representative 3 0.9% perceived as part of the sharing economy (Table 5) might have in- Insurance company 3 0.9% centives to enact a discursive strategy aimed at building legitimacy for Total 337 100.0% their respective organizations (Creed et al., 2002; de Holan and Phillips, 2002; Dorado, 2005). As described in Section 2, the term sharing economy and the related notion of collaborative consumption seem to With regard to the presented actors and their associated practices, only have encompassed two opposite forces, hence two institutional logics fi fi 1 (Delbar) of the total number of 31 identi ed actors can be de ned as have coexisted which, in turn, seems to have been an enabling condi- providing a sharing-practice-based operation (cf. Table 3). The 11 re- tion for this plethora of sharing-economy actors to take part in the ferences to this actor corresponded to 2.8% of the total material. When framing of the sharing economy (Sewell, 1992; Clemens and Cook, fi taken together, these ndings illustrate the diverse character of the 1999). sharing economy whilst at the same time providing strong indications When taken together, and considering the definition suggested by of how sharing-economy actors using a market logic are subject to most Möhlmann (2015) on the interplay between collaborative consumption fi attention in the eld. and the sharing economy, our findings are only partially compatible fi Table 6 presents the actor groups identi ed as contributing the most with this definition due to the considerable variance of how the sharing to the framing of the sharing economy during the study period, along economy is framed and the diverse set of actor groups that influence with the frequency and share of the user-generated content these actor that framing. Therefore, we propose a new and expanded definition of groups produced in relation to the total material of 999 user-generated the sharing economy, which explicitly captures both market and non- fi posts. The table identi es sharing-economy actors as the dominant market logics and practices: group contributing to meanings and values associated with the orga- nizational field of the sharing economy. Parallel to their dominance, ICT-enabled platforms for exchanges of goods and services drawing however, actor groups from other sectors also contribute to the framing on non-market logics such as sharing, lending, gifting and swapping of the field. Two examples from the data were published on Twitter on as well as market logics such as renting and selling. April 3, 2016 by a politician, and on May 12, 2016 by a sharing- economy actor: 5. Concluding remarks, limitations, and directions for future “Seems like it blows up a political dispute over the sharing economy and research Uber between the right and the left. Good. The left will lose.” This paper has explored how the sharing economy is framed whilst “ ” We need transportation, not owning vehicles! also identifying the main actors driving current developments. In ad- dition, this paper has provided a structured empirical contribution, revealing that the sharing economy in Sweden includes a plethora of practices relating to both non-market and market logics. Our findings also highlight several issues which remain unsolved and which, in this

63 C. Laurell, C. Sandström Technological Forecasting & Social Change 125 (2017) 58–65 context, remain controversial, e.g., taxation and regulation. Therefore, Bohnsack, R., Pinkse, J., Waelpoel, A., 2016. The institutional evolution process of the global solar industry: the role of public and private actors in creating institutional our results suggest that the contemporary sharing economy should be shifts. Environ. Innov. Soc. Transit. 20, 16–32. regarded as an emergent and fluid field where tensions between market Botsman, R., Rogers, R., 2010. What's Mine is Yours. Collins, London. and non-market logics create a state of instability. In view of these Bourdieu, P., 1984. Sociology in Question. Les Editions de Minuit, Paris. fi fi Bourdieu, P., 1990. The Logic of Practice. Polity Press, Cambridge. ndings and extant literature, we provide an expanded de nition of the Clemens, E.S., Cook, J.M., 1999. 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York. Christofer Laurell is Jan Wallander postdoctoral researcher at the Center for Sports and Stieglitz, S., Dang-Xuan, L., Bruns, A., Neuberger, C., 2014. Social media analytics. Bus. Business, Stockholm School of Economics Institute for Research, and an assistant pro- Inf. Syst. Eng. 6 (2), 89–96. fessor at Jönköping International Business School. His research interests focus on in- Thornton, P.H., Ocasio, W., 1999. Institutional logics and the historical contingency of stitutional pressures created by the rise of social media and its implications for marketing. power in organizations: executive succession in the higher education publishing in- – – dustry, 1958 1990. Am. J. Sociol. 105 (3), 801 843. Christian Sandström is associate professor in innovation management at Chalmers Wang, C., Zhang, P., 2012. The evolution of social commerce: the people, management, University of Technology and the Ratio Institute in Sweden. His research concerns the – technology, and information dimensions. Commun. Assoc. Inf. Syst. 31 (1), 105 127. interplay between technological and institutional change.

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